startMarks function

startMarks: Function used to initialize a marked-point process model.

This function is used to create an object containing all the data, metadata and relevant components required for the integrated species distribution model and INLA to work. As a result, the arguments associated with this function are predominantly related to describing variable names within the datasets that are relevant, and arguments related to what terms should be included in the formula for the integrated model. The output of this function is an R6 object, and so there are a variety of public methods within the output of this function which can be used to further specify the model (see ?specifyMarks for a comprehensive description of these public methods).

startMarks(
  ...,
  spatialCovariates = NULL,
  Projection,
  Mesh,
  IPS = NULL,
  Boundary = NULL,
  markNames = NULL,
  markFamily = NULL,
  marksSpatial = TRUE,
  pointCovariates = NULL,
  pointsIntercept = TRUE,
  marksIntercept = TRUE,
  Offset = NULL,
  pointsSpatial = "copy",
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  trialsMarks = NULL,
  temporalName = NULL,
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)

Arguments

• ...: The datasets to be used in the model. Must come as either sf objects, or as a list of named sf objects.
• spatialCovariates: The spatial covariates used in the model. These covariates must be measured at every location (pixel) in the study area, and must be a SpatialRaster object. Can be either numeric, factor or character data. Defaults to NULL which includes no spatial effects in the model.
• Projection: The coordinate reference system used by both the spatial points and spatial covariates. Must be of class character.
• Mesh: An fm_mesh_2d object required for the spatial random fields and the integration points in the model (see fm_mesh_2d_inla from the fmesher package for more details).
• IPS: The integration points to be used in the model (that is, the points on the map where the intensity of the model is calculated). See fm_int from the fmesher package for more details regarding these points; however defaults to NULL which will create integration points from the Mesh and Boundaryobjects.
• Boundary: A sf object of the study area. If not missing, this object is used to help create the integration points.
• markNames: A vector with the mark names (class character) to be included in the integrated model. Marks are variables which are used to describe the individual points in the model (for example, in the field of ecology the size of the species or its feeding type could be considered). Defaults to NULL, however if this argument is non-NULL, the model run will become a marked point process. The marks must be included in the same data object as the points.
• markFamily: A vector with the statistical families (class character) assumed for the marks. Must be the same length as markNames, and the position of the mark in the vector markName is associated with the position of the family in markFamily. Defaults to NULL which assigns each mark as "Gaussian".
• marksSpatial: Logical argument: should the marks have their own spatial field. Defaults to TRUE.
• pointCovariates: The non-spatial covariates to be included in the integrated model (for example, in the field of ecology the distance to the nearest road or time spent sampling could be considered). These covariates must be included in the same data object as the points.
• pointsIntercept: Logical argument: should the points be modeled with intercepts. Defaults to TRUE. Note that if this argument is non-NULL and pointsIntercepts is missing, pointsIntercepts will be set to FALSE.
• marksIntercept: Logical argument: should the marks be modeled with intercepts. Defaults to TRUE.
• Offset: Name of the offset variable (class character) in the datasets. Defaults to NULL; if the argument is non-NULL, the variable name needs to be standardized across datasets (but does not need to be included in all datasets). The offset variable will be transformed onto the log-scale in the integrated model.
• pointsSpatial: Argument to determine whether the spatial field is shared between the datasets, or if each dataset has its own unique spatial field. The datasets may share a spatial field with INLA's "copy" feature if the argument is set to copy. May take on the values: "shared", "individual", "copy" or NULL if no spatial field is required for the model. Defaults to "shared".
• responseCounts: Name of the response variable in the counts/abundance datasets. This variable name needs to be standardized across all counts datasets used in the integrated model. Defaults to 'counts'.
• responsePA: Name of the response variable (class character) in the presence absence/detection non-detection datasets. This variable name needs to be standardized across all present absence datasets. Defaults to 'present'.
• trialsPA: Name of the trials response variable (class character) for the presence absence datasets. Defaults to NULL.
• trialsMarks: Name of the trials response variable (class character) for the binomial marks (if included). Defaults to NULL.
• temporalName: Name of the temporal variable (class character) in the model. This variable is required to be in all the datasets. Defaults to NULL.
• Formulas: A named list with two objects. The first one, covariateFormula, is a formula for the covariates and their transformations for the distribution part of the model. Defaults to NULL which includes all covariates specified in spatialCovariates into the model. The second, biasFormula, specifies which covariates are used for the PO datasets. Defaults to NULL which includes no covariates for the PO datasets.

Returns

A specifyMarks object (class R6). Use ?specifyMarks of .$help() to get a comprehensive description of the slot functions associated with this object.

Note

The idea with this function is to describe the full model: that is, all the covariates and spatial effects will appear in all the formulas for the datasets and species. If some of these terms should not be included in certain observation models in the integrated model, they can be thinned out using the .$updateFormula function. Note: the point covariate and mark terms will only be included in the formulas for where they are present in a given dataset, and so these terms do not need to be thinned out if they are not required by certain observation models.

Examples

if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
                      
 }